Modelling Body Mass Index Distribution using Maximum Entropy Density
The objective of this paper is to model the distribution of Body Mass Index (BMI) for a given set of covariates. BMI is one of the leading indicators of health and has been studied by health professionals for many years. As such, there have been various approaches to model the distribution of BMI....
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
2015
|
| Online Access: | http://hdl.handle.net/20.500.11937/78300 |
| _version_ | 1848763956248707072 |
|---|---|
| author | Singh, Ranjodh Chan, F. Harris, Mark |
| author2 | Weber, T |
| author_facet | Weber, T Singh, Ranjodh Chan, F. Harris, Mark |
| author_sort | Singh, Ranjodh |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The objective of this paper is to model the distribution of Body Mass Index (BMI) for a given set
of covariates. BMI is one of the leading indicators of health and has been studied by health professionals for
many years. As such, there have been various approaches to model the distribution of BMI. Furthermore, there
are numerous studies which investigate the association between an individual’s physical and socio-economic
attributes (covariates) to their BMI levels. This paper proposes the use of Maximum Entropy Density (MED)
to model the distribution of BMI using information from covariates. The paper shows how covariates can be
incorporated into the MED framework. This framework is then applied to an Australian data set. The results
show how different covariates affect different moments of the estimated BMI distribution. |
| first_indexed | 2025-11-14T11:11:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-78300 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:11:42Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-783002020-06-09T07:41:15Z Modelling Body Mass Index Distribution using Maximum Entropy Density Singh, Ranjodh Chan, F. Harris, Mark Weber, T McPhee, MJ Anderssen, RS The objective of this paper is to model the distribution of Body Mass Index (BMI) for a given set of covariates. BMI is one of the leading indicators of health and has been studied by health professionals for many years. As such, there have been various approaches to model the distribution of BMI. Furthermore, there are numerous studies which investigate the association between an individual’s physical and socio-economic attributes (covariates) to their BMI levels. This paper proposes the use of Maximum Entropy Density (MED) to model the distribution of BMI using information from covariates. The paper shows how covariates can be incorporated into the MED framework. This framework is then applied to an Australian data set. The results show how different covariates affect different moments of the estimated BMI distribution. 2015 Conference Paper http://hdl.handle.net/20.500.11937/78300 10.36334/MODSIM.2015.E5.chan2 http://creativecommons.org/licenses/by/4.0 fulltext |
| spellingShingle | Singh, Ranjodh Chan, F. Harris, Mark Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title | Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title_full | Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title_fullStr | Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title_full_unstemmed | Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title_short | Modelling Body Mass Index Distribution using Maximum Entropy Density |
| title_sort | modelling body mass index distribution using maximum entropy density |
| url | http://hdl.handle.net/20.500.11937/78300 |